Data & AI Scientist
Role details
Job location
Tech stack
Job description
We are looking for an experienced and agile Data & AI Scientist to join our dynamic team. In this role, you will design, develop, and deploy end-to-end AI systems that transform complex data and business challenges into scalable, production-grade intelligence solutions.
Your work will combine the rigor of data science with the practicality of AI engineering, ensuring that advanced analytics and machine learning solutions deliver measurable business value across multiple domains.
Your expertise in machine learning, time-series analysis, and advanced analytics will support multidisciplinary initiatives, particularly in predictive maintenance, operational optimization, and commercial intelligence. You will collaborate closely with cross-functional teams to translate ideas into impactful AI solutions while applying robust engineering practices, responsible AI principles, and modern MLOps frameworks to ensure scalability, security, reliability, and compliance.
Your Role
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Translate complex business challenges into scalable AI/ML solutions that deliver measurable business impact.
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Design, develop, train, validate, and deploy end-to-end AI systems across the full lifecycle, including data exploration, modeling, deployment, monitoring, and continuous improvement.
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Develop predictive maintenance models using sensor, equipment, and IoT data to optimize uptime, reliability, and operational performance.
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Apply machine learning techniques to commercial use cases such as churn prediction, customer segmentation, forecasting, and recommendation systems.
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Collaborate cross-functionally with product managers, software engineers, data engineers, architects, and business stakeholders to ensure alignment and successful delivery.
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Build scalable and automated ML pipelines using cloud-native platforms such as Azure ML, AWS SageMaker, and Databricks.
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Contribute to AI engineering best practices, MLOps standards, model governance, and responsible AI adoption across projects.
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Stay current with emerging AI technologies, including generative AI, agentic AI, and LLM-based applications, and identify opportunities to apply them effectively.
Requirements
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You hold a Master's or PhD in Computer Science, Data Science, Artificial Intelligence, Machine Learning, or a related technical field.
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You have 5+ years of hands-on experience with a Master's degree, or 3+ years with a PhD, building and deploying AI/ML solutions in enterprise environments.
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You possess strong expertise in machine learning, predictive analytics, and time-series modeling.
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You are highly proficient in Python, SQL, and Spark, with hands-on experience using frameworks such as Scikit-learn, TensorFlow, and PyTorch.
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You have experience with MLOps tools and practices, including MLflow, CI/CD pipelines, model monitoring, and automated deployment workflows.
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You are experienced with cloud-native AI/ML platforms such as Azure ML, AWS SageMaker, and Databricks.
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You communicate effectively with both technical and non-technical stakeholders.
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You have experience in predictive maintenance, commercial AI, industrial AI, or similar applied AI domains.
Preferred Qualifications
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Experience with large language models (LLMs), generative AI, retrieval-augmented generation (RAG), or agentic AI frameworks.
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Familiarity with responsible AI, data governance, model explainability, and AI risk management practices.
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Experience leading AI initiatives from concept through production deployment and operationalization.